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Modeling polyphonic music is a particularly challenging task because of theintricate interplay between melody and harmony. A good model should satisfythree requirements: statistical accuracy (capturing faithfully the statisticsof correlations at various ranges, horizontally and vertically), flexibility(coping with arbitrary user constraints), and generalization capacity(inventing new material, while staying in the style of the training corpus).Models proposed so far fail on at least one of these requirements. We propose astatistical model of polyphonic music, based on the maximum entropy principle.This model is able to learn and reproduce pairwise statistics betweenneighboring note events in a given corpus. The model is also able to invent newchords and to harmonize unknown melodies. We evaluate the invention capacity ofthe model by assessing the amount of cited, re-discovered, and invented chordson a corpus of Bach chorales. We discuss how the model enables the user tospecify and enforce user-defined constraints, which makes it useful forstyle-based, interactive music generation.

Style Imitation and Chord Invention in Polyphonic Music with Exponential Families